code: def _eval_input_receiver_fn(tf_transform_output, schema):
      """Build everything needed for the tf-model-analysis to run the model.
  
      Args:
        tf_transform_output: A TFTransformOutput.
        schema: the schema of the input data.
  
      Returns:
        EvalInputReceiver function, which contains:
          - Tensorflow graph which parses raw untransformed features, applies the
            tf-transform preprocessing operators.
          - Set of raw, untransformed features.
          - Label against which predictions will be compared.
      """
      # Notice that the inputs are raw features, not transformed features here.
      raw_feature_spec = _get_raw_feature_spec(schema)
  
      serialized_tf_example = tf.compat.v1.placeholder(
          dtype=tf.string, shape=[None], name="input_example_tensor"
      )
  
      # Add a parse_example operator to the tensorflow graph, which will parse
      # raw, untransformed, tf examples.
      features = tf.io.parse_example(
          serialized=serialized_tf_example, features=raw_feature_spec
      )
  
      # Now that we have our raw examples, process them through the tf-transform
      # function computed during the preprocessing step.
      transformed_features = tf_transform_output.transform_raw_features(
          features
      )
  
      # The key name MUST be 'examples'.
      receiver_tensors = {"examples": serialized_tf_example}
  
      # NOTE: Model is driven by transformed features (since training works on the
      # materialized output of TFT, but slicing will happen on raw features.
      features.update(transformed_features)
  
      return tfma.export.EvalInputReceiver(
          features=features,
          receiver_tensors=receiver_tensors,
          labels=transformed_features[_transformed_name(_LABEL_KEY)],
      )
  
file_path: tfx/tfx/examples/bigquery_ml/taxi_utils_bqml.py
imports: from typing import List
  import tensorflow
  from tensorflow import estimator
  import tensorflow_model_analysis
  import tensorflow_transform
  from tensorflow_transform.tf_metadata import schema_utils
  from tfx.components.trainer.fn_args_utils import DataAccessor
  from tfx_bsl.tfxio import dataset_options
label_desc: Use image node_id 8 for calling a global function with example usage: _eval_input_receiver_fn(tf_transform_output, schema) and returns: tfma